• Proceedings of the Korea Information Processing Society Conference
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• 2014.11a
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• pp.225-226
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• 2014

본 연구에서는 회귀분석을 통해 오피스 건물의 에너지 사용량을 예측하는 시스템을 개발하고 이를 실증하는 실험적 연구를 수행하였다. 회귀분석의 파라미터로는 외기온도, 에너지 사용량 등이 사용되었으며 예측 정확도 향상을 위해 파라미터를 확장해서 실험하였다. 에너지 사용량 예측에 대한 검증을 위해서 실시간 데이터 수집과 분석을 위한 시스템을 개발하였으며, 해당 시스템을 이용해 수원 소재 오피스 건물에서 실증한 결과에 따르면 겨울철 에너지 사용량에 대한 예측 오차율이 10% 미만으로 나타났다.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.571-574
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• 2016

최근 에너지 효율을 최적화하는 차세대 지능형 전력망인 스마트 그리드 시스템(Smart Grid System)이 국내외에 널리 보급되고 있다. 그로 인해 그리드 시스템의 효율적인 운영을 위해 적용되는 EMS(Energy Management System) 기술의 중요성이 커지고 있다. EMS는 에너지 사용량 예측의 높은 정확성이 요구되며, 예측이 정확하게 수행될수록 에너지의 활용성이 높아진다. 본 논문은 전력 사용량 예측의 정확성 향상을 위한 새로운 기법을 제안한다. 구체적으로, 먼저 사용량에 영향을 미치는 환경적인 요인들을 분석한다. 분석된 요인들을 적용하여 유사한 환경을 가지는 전력 사용량 데이터의 사전 군집화를 수행한다. 그리고 예측 일에 관련된 환경 정보와 가장 유사한 군집의 전력 사용량 데이터를 기반으로 전력 사용량을 예측한다. 제안하는 기법의 성능을 평가하기 위해, 다양한 실험을 통하여 일간 전력 사용량을 예측하고 그 정확성을 측정하였다. 결과적으로, 기존의 기법들과 비교했을 때, 최대 52.88% 향상된 전력 사용량 예측 정확성을 보였다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.5
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• pp.637-645
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• 2019

Global warming and a new energy policy request the energy saving and pollutant emission control in municipal level. Previous studies focus on transportation in the Seoul metropolitan area which can easily meet the policy goal by reducing it. This study expands the area of urban energy planning to the industries and land use which takes up most of energy use of the city. We empirically study the Busan metropolitan area's 5 years natural gas and electricity consumption data by the industries and land use. Results show that energy usage significantly depends on not only population but also urbanizing intensity and industrial category. This paper address that the policy maker need to pay attention on energy usage pattern of each sectors during the planning.

• Journal of Internet Computing and Services
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• v.24 no.2
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• pp.57-66
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• 2023

Currently, a new energy system is emerging that implements consumption reduction by improving energy efficiency. Accordingly, as smart grids spread, the rate system by timing is expanding. The rate system by timing is a rate system that applies different rates by season/hour to pay according to usage. In this study, external factors such as temperature/day/time/season are considered and the time series prediction model, LSTM, is used to predict energy power usage data. Based on this energy usage prediction model, energy usage charges are reduced by analyzing usage patterns for each device and transferring power energy from the maximum load time to the light load time. In order to analyze the usage pattern for each device, a clustering technique is used to learn and classify the usage pattern of the device by time. In summary, this study predicts usage and usage fees based on the user's power data usage, analyzes usage patterns by device, and provides customized demand transfer services based on analysis, resulting in cost reduction for users.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1208-1210
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• 2015

본 논문은 실제 사무용 빌딩의 전력 사용량 빅 데이터를 이용하여 효율적인 에너지 사용 및 빌딩 에너지 수요 반응 관리를 위해 전력 사용량 기준 클러스터링을 통한 분석결과를 제시하고 분석된 클러스터링 결과를 기반으로 전력 사용량 예측모델을 설계하였다. 예측모델을 통해 도출된 전력 사용량과 실제 빌딩의 실측 데이터 사이의 오차율을 계산하여 예측모델의 타당성을 보였으며 이를 토대로 에너지 예측 사용량 기반 효율적인 수요 반응 제어 알고리즘을 제시하였다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.3
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• pp.175-181
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• 2021

This paper analyzes the artificial intelligence-based approach for short-term energy consumption prediction. In this paper, we employ the reinforcement learning algorithms to improve the limitation of the supervised learning algorithms which usually utilize to the short-term energy consumption prediction technologies. The supervised learning algorithm-based approaches have high complexity because the approaches require contextual information as well as energy consumption data for sufficient performance. We propose a deep reinforcement learning algorithm based on multi-agent to predict energy consumption only with energy consumption data for improving the complexity of data and learning models. The proposed scheme is simulated using public energy consumption data and confirmed the performance. The proposed scheme can predict a similar value to the actual value except for the outlier data.

• Journal of the Korean Solar Energy Society
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• v.34 no.6
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• pp.93-102
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• 2014

The aim of this study is to analyze the influence of various factors on energy consumption of apartment buildings. Energy consumption data of the Green Together, integrated building energy management system maintained by the government were used, and end-use and primary energy consumption data of 2012 were analyzed for 181 apartment complexes completed between 2004 and 2011 in Seoul. Energy consumption by use, source and heating type were analyzed. Then, energy consumption trends were analyzed and suggested according to energy efficiency ratings, number of households, areas for exclusive use, number of floors, core types, building types, orientations and completion years.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.24 no.6
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• pp.131-137
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• 2010

Change of the energy used in a house can be separated from LNG, and electric power. The electric power consumption of a house is more increasing than LNG. The interest for electric houses is rising due to energy saving and low carbon emission. Accordingly, the amount of energies and cost are analyzed consumed in a house using cumulative energy consumption. The result of analysis, amount of electric power, is more increase. In comparison, the use volume of city gas is more decrease. In this paper, the use volume of energy resource is analyzed using historical energy consumption data in the past 25 years. In addition, expected electrical power and heating energy is analysed adopting an Electric Residence.

• Land and Housing Review
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• v.12 no.3
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• pp.79-85
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• 2021

This study analyzed the energy consumption differences between rental and owner owned (purchased) apartments in Busan, South Korea during the cooling and heating seasons. Analysis revealed that the average electricity consumed for cooling was 2.5 kWh/m²·yr for rental apartments and 2.3 kWh/m²·yr for purchased apartments, a difference of 0.2 kWh/m²·yr. The average electrical heating energy consumption was 3.3 kWh/m²·yr for rental apartments and 2.2 kWh/m²·yr for purchased apartments, a difference of 1.1 kWh/m²·yr. It was estimated that the use of electric blankets and heaters was higher in rental apartments than purchased apartments resulting in higher electrical heating energy consumption. The average gas heating energy consumption was 7.0 kWh/m²·month for rental apartments and 6.8 kWh/m²·month for purchased apartments. When electricity and gas usage was combined for heating, the average total heating energy consumption was 10.3 kWh/m²·month for rental apartments and 9.0 kWh/m²·month for purchased apartments. This indicates that rental apartments consume 1.3 kWh/m²·month more energy than purchased apartments during heating season. Overall, rental apartments consume more energy than purchased apartments during both the cooling and heating seasons.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.1057-1059
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• 2011

본 논문은 홈/빌딩네트워크 환경에서 에너지 절감을 위한 효율적인 관리방법을 제안한다. 보다 상세하게는 먼저 가정(home)이나 빌딩(building) 등의 건물에서 사용되는 에너지의 정보를 수집하여 이들에 관한 통합 자원 관계맵을 구축한다. 구축된 자원 관계맵을 기반으로 실제 에너지 사용량과 금액을 계산하여 알려줄 뿐만 아니라 에너지의 예측 사용량과 금액을 계산하여 사용자에게 알려준다. 이와 더불어 사용량이 많을 경우 각 자원별 뿐만 아니라 지역, 사용량, 타입, 특성과 같은 다양한 그룹별로 에너지 절감 방안을 제시함으로써 사용자가 실제로 에너지를 효과적으로 절감할 수 있도록 하는 자원관계맵 기반의 에너지 절감방안을 제안한다.